Libraries Used

• Our analysis for this presenation would not have been possible without these libaries:

library(survival)
library(KMsurv)
library(ggplot2)
library(ggpubr)
library(survminer)
library(plotly)
library(muhaz)
library(ggthemes)
library(plyr)

data(std)
std

## [1] FALSE

Exploratory Data Analysis

Take a look at correlation between symptoms and different infection types.

Is it more common for one type of infection based on type of sex (oral/anal).

Checking Proportionality

• In order to build Cox model, we need to check that the data meets our proportional hazards assumptions. We will check this with Kaplan-Meier Curves and Hazard Ratios.

Kaplan-Meier Curves

## Call:
## survdiff(formula = surv_object ~ iinfct, data = std)
## 
##                    N Observed Expected (O-E)^2/E (O-E)^2/V
## iinfct=gonorrhea 140       73     54.5   6.28042   7.50617
## iinfct=chlamydia 396      135    153.0   2.12201   3.81136
## iinfct=both      341      139    139.5   0.00166   0.00278
## 
##  Chisq= 8.5  on 2 degrees of freedom, p= 0.01

• Since we see no intersection in our Kaplan-Meier Curves for each type of initial infection, we can concur proportional hazards hold.

Hazard Ratios

Cumulative Hazards and CLogLog

Schoenfield Residuals for Initial Infection

• P-value here is large, so we do not have a violation of proportional hazards.

## Call:
## coxph(formula = surv_object ~ iinfct, data = std)
## 
##   n= 877, number of events= 347 
## 
##                    coef exp(coef) se(coef)      z Pr(>|z|)   
## iinfctchlamydia -0.4202    0.6569   0.1457 -2.884  0.00393 **
## iinfctboth      -0.2980    0.7423   0.1450 -2.055  0.03984 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                 exp(coef) exp(-coef) lower .95 upper .95
## iinfctchlamydia    0.6569      1.522    0.4937    0.8741
## iinfctboth         0.7423      1.347    0.5587    0.9863
## 
## Concordance= 0.524  (se = 0.016 )
## Likelihood ratio test= 7.93  on 2 df,   p=0.02
## Wald test            = 8.37  on 2 df,   p=0.02
## Score (logrank) test = 8.46  on 2 df,   p=0.01

Building a full model

• To better understand the influences on reinfection, we build a cox Proportional Hazard Model with all of the variables in the data

cox1 <-
  coxph(
    surv_object ~ iinfct + marital + race + os12m + os30d +
      rs12m + rs30d + abdpain + discharge + dysuria + condom + 
      itch + lesion + rash + lymph + vagina + dchexam + abnode +
      age + yschool + npartner,
    data = std
  )

• We then use the drop1() function to examine how the dropping of each variable influences the AIC of the model.

Simplifying the Model

Tranformation of Variables

Residuals

Outliers

In Conclusion